Gait analysis with videogrammetry can differentiate healthy elderly, mild cognitive impairment, and Alzheimer's disease: A cross-sectional study

Abstract Gait parameters have been investigated as an additional tool for differential diagnosis in neurocognitive disorders. A videogrammetry system could be used to capture and analyze gait in older adults. Different motor conditions were used in three specific assessments: 10-m walk test (10mWT), timed up and go test (TUGT) and treadmill walk test (TWT). These tasks were compared among healthy elderly (HE = 17), mild cognitive impairment (MCI = 23) and Alzheimer's disease (AD = 23) patients. The aim was to select the better gait parameter to differentiate these groups among different motor tests conditions with videogrammetry analyses. One-way ANOVA, Kruskal-Wallis, and Bonferroni post-hoc test were used to compare variables among groups. Then, an effect size (ES) and a linear regression analysis were calculated. The gait parameters showed significant differences among groups in all tests conditions, but not in TWT. Gait velocity (controlled by confounding variables) in 10mWT and TUGT at usual speed and dual-task condition, predicts 39% and 53% of the difference among diagnoses, respectively. We concluded that a low-cost and practical video analysis could be able to differentiate HE, MCI, and AD in clinical assessments.